In this project, we propose a combined experimental and computational investigation of the origins and consequences of stochasticity in gene expression in the single-cell eukaryote Saccharomyces cerevisiae. The goal of this work is to gain insight into the role that stochasticity, or noise, plays in the dynamics of eukaryotic gene regulation and expression, and the mechanisms through which gene expression noise arises. We have previously shown, using a combined experimental and computational approach that transcriptional efficiency and the mechanism of transcriptional control directly relate to the level of noise in gene expression from certain eukaryotic promoters. We have also demonstrated a plausible link between codon usage and gene expression noise, indicating that a possible determinant of gene expression noise may be encoded in the genome. Moreover, we have shown that increased noise in a regulatory protein can have profound effects on bimodal responses, which can be critical for cellular differentiation and the maintenance of phenotypic variation within a clonal population of cells. These results warrant further study of the specific mechanisms involved in determining the level of noise in gene expression, as well as how such noise can directly affect cell phenotype. Accordingly, the specific aims of this project are: (1) to determine how the process of transcriptional reinitiation affects the level of heterogeneity in the expression of a given gene; (2) to examine how codon usage can modulate the level of heterogeneity in the expression of a gene; (3) to examine the effects of noise on graded versus bimodal responses of a single promoter in a clonal population of cells; and (4) to explore the phenotypic consequences of increased noise in the expression of a key regulatory gene involved in a well-studied cellular process. We will couple our experimental findings to stochastic models of gene expression to assist in determining specific mechanisms in the processes of transcription and translation that affect noise. By combining experimental studies with computational models, we aim to more clearly understand how genetic variables, such as gene and promoter sequence can modulate gene expression noise, and how such noise can affect cell phenotype. Our goal is to develop tools, which can be used to better predict the effects of noise on cell behavior, thereby enabling more precise control of gene expression and cell function.